Abstract
The proliferation of deepfakes—AI-generated or manipulated media—has transformed the landscape of contemporary art. Deep generative models, including GANs, VAEs, diffusion models, and Transformers, have enabled artists to explore new creative realms while simultaneously raising critical questions around authenticity, ethics, and detection. This paper presents a comprehensive analysis of deepfake technologies across five key media modalities: image, video, text, speech, and music. We examine the architectures that enable content creation, and the state-of-the-art techniques used for detection. Further, we evaluate detection accuracy, robustness, and practical implementation, incorporating diagrams, comparative tables, and performance formulas. This work aims to provide a balanced perspective on the opportunities and challenges posed by synthetic media in the artistic domain.